Maintaining a high-quality big-data analytics model may not be easy when training data continue to change. For example, event-processing middleware can support high-quality reactions to events by providing context to the event agents. When this context consists of a large amount of data, it helps to train an analytics model for it. In a continuously running solution, this model should be kept up-to-date, otherwise quality degrades. However, ripple-through effects make training (whether from scratch or incremental) expensive.